Nowadays, the Web contains a lot of webpages providing information intended to beprocessed by humans. Many of these pages need data storage with dynamical dataloading and for these reasons they

use object-relational databases. But what they don’thave is fully linked content. The Semantic Web is based on a different concept. In theworld of sematic datasets there are many semantic databases which are linked together.Therefore, we can use thesedatabases for answering more complex queries than usingtraditional keyword-based search engines.

The easiest way for this user is to ask forinformation in natural language. Remember, how many times you have written asentence like “How to do something”.This type of query is now rare on the Web.

There already has been some research done in the field of querying data usingnatural language

on classical databases [1].

In field of semantic databases there is toosome methods like [2,3].

Pre-processing is a key part of the natural language interface, as we mentionedearlier, therefore it is also in the method we propose. We scan the wholedataset andcreate two lexicons:Classes and properties lexicon,Values lexicon.

The first lexicon isbased on structural part of our dataset. It consists of the names,labels etc. of all classes and properties. Next, all structural parts are decorated withsynonyms from WordNet, which allows us to formulate query using different wordsthan the ones used in the dataset. We call these alternative namesdescriptors

and weprovide ranking based on their source.

The second lexicon, using of which is completely new in our approach and noneof the examined methods uses it, consists of property values that were obtained duringthe pre-processing phase. When the user types a value to his query, this lexicon cannavigate us to an object type, which contains this value.

One of the main points of our method is processing of transformation to onto-dictionary

language. In this process we identified modifiers in user query and add them toSPARQL request.

Figure1

Query processor schema

Next, we plan to evaluate our method with experiment using Annota

Firefox extension.We will enhance the ACM digital library web site with our own search engine. We fillour dataset with data produced by Annota which currently has metadata from variousdigital libraries (ACM, IEEE, etc.) and store them in an ontological

dataset.

Amended version was published in Proc. of the 9th Student Research Conference inInformatics and Information Technologies (IIT.SRC 2013), STU Bratislava, xx-xx.